使用由遗传算法优化的自编码器组合进行破产预测.
Róbert Kanász1, Peter Gnip1, Martin Zoričák2
1Department of Computers and Informatics, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Košice, Slovakia.
PeerJ. Computer science
|June 22, 2023
概括
这项研究引入了一种新的破产预测方法,使用由遗传算法优化的自编码组合. 该方法有效地识别了面临破产风险的公司,在各种数据集中实现了高准确性.
科学领域:
- 金融建模金融建模
- 机器学习用于业务分析.
- 计算金融是一种计算金融.
背景情况:
- 预测企业破产对于金融机构和利益相关者来说至关重要.
- 由于复杂的影响因素和不平衡的数据集,破产预测具有挑战性.
- 现有的方法经常与破产数据的扭曲性质作斗争.
研究的目的:
- 开发一个强大的破产预测模型.
- 在破产预测中解决不平衡数据集的挑战.
- 提高识别面临财务困境的公司的准确性和可靠性.
主要方法:
- 使用浅层的自动编码器组合来学习数据分布.
- 采用遗传算法来优化自编码器的分类值.
- 在中小企业的不平衡数据集上训练模型.
- 使用多个数据集的几何平均得分来评估性能.
主要成果:
- 自动编码器组合在识别破产公司方面表现强.
- 几何平均得分从71%到93.7%不等,表明有效的预测能力.
- 遗传算法成功优化了分类值,以提高准确性.
- 该方法在不同行业和评估期间证明有效.
结论:
- 由遗传算法优化的拟议的浅层自编码器组合为破产预测提供了强大的解决方案.
- 这种方法有效地处理不平衡的数据,这是金融风险评估中的一个常见挑战.
- 该模型的高性能表明它对银行,政府机构和企业主来说是有用的.
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